Applying a Decomposed Theory of Planned Behavior ...

Applying a Decomposed Theory of Planned Behavior to Study the Intentions of Paying Behaviors in a Virtual Community Shwu-Min Horng, Yih-Yuh Lee, Chih-Lun Wu

Applying a Decomposed Theory of Planned Behavior to Study the Intentions of Paying Behaviors in a Virtual Community 1.Shwu-Min Horng, 2.Yih-Yuh Lee, 3.Chih-Lun Wu Department of Business Administration, National Chengchi Universiy,[email protected] Department of Business Administration, National Chengchi University, [email protected] Department of Business Administration, National Chengchi University ,[email protected]

Abstract In this study, we adopt the decomposed theory of planned behavior as the research framework and apply constructs of decomposed attitudes (i.e., website service quality, website satisfaction, and website loyalty), subjective norms, and perceived behavior control to explore the factors affecting the intentions of online users to become paying members in virtual communities. Structural equation modeling is applied to analyze the research framework. Some research findings are proposed.

Keywords: Web 2.0, Virtual Community, Paying Intention, Theory Of Planned Behavior 1.Introduction An extraordinarily fast-growing online virtual community has developed from the success of social collaborative technologies such as Wikis, blogs, and location-based services (LBS) through which online users share information, communicate, and maintain relationships [1]. The virtual community in particular can generate revenue from three different resource models—advertising, transaction, and subscription. The purposes of these different models are to increase net flows, attract users to buy virtual currency or goods, and increase the number of paying users with better services to build trust between users and website owners separately. Regarding the advertising model, websites must accumulate a certain amount of members to profit from better advertising prices [2]. In addition, the online advertising market in the U.S. has been mostly occupied by Google and Yahoo, at 14.7% and 13.3%, respectively [3]. It is difficult for newcomers and developing websites with virtual communities to sell advertising to firms at better prices than these big firms. For the transaction model, online users who utilize free services have established an expectation for more free services online [4]. Even if website operators succeed in attracting users to participate in specific website activities, they risk losing users if they begin to charge for services previously offered for free. Users might visit other sites with similar services because the switching costs are relatively low. In the subscription model, virtual community sites make profits by charging membership fees. Websites usually attract a large number of users to register as members based on free services. The websites may charge heavy fees to users, who may seek and respond favorably to fee-based value-added services. Compared to the advertising and transaction models, the subscription model is a reliable source of revenue, especially for newly developing virtual community sites. The stable and sufficient revenues of membership fees can be a good business model to avoid competing directly with large-scale virtual community websites in the online advertisement market. In this study, we intend to provide suggestions to establish new or small-scale virtual community website operators. The purpose of this study is to expand the decomposed theory of planned behavior (DTPB) model to explain users’ intentions of paying subscriptions in the virtual community.

2.Literature review The theory of reasoned action (TRA) was developed based on social psychology [5]. The main purpose of this theory is to predict behavioral intentions and to understand the human-specific behaviors behind intentions. The theory consists of three basic constructs: behavioral intentions (BI), attitudes, and subjective norms (SN). Behavioral intentions will predict whether or not the individual

International Journal of Intelligent Information Processing(IJIIP) Volume3. Number2. June. 2012. doi: 10.4156/IJIIP.vol3.issue2.3

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will engage in a particular behavior. That behavior is determined by attitudes and SN, which are composed of behavioral beliefs and normative beliefs, respectively. Moreover, the theory of planned behavior (TPB) is the extension of TRA. In TPB, behaviors are influenced by attitude and SN, as well as by perceived behavioral control (PBC) [6]. PBC refers to individuals’ perceptions of the ease or difficulty of performing the behavior of interest [7]. In addition, the individual must deal with situations in which he or she might lack complete volitional control over the behavior of interest. Hence, individuals’ intended behavior is affected by attitudes and SN, as well as by external conditions, such as opportunities and resources (e.g., money, time, skills, and cooperation of others) in increasing behavioral intentions [8]. Otherwise, individuals are unable to engage in certain behaviors with behavioral intentions. Thus, attitude, SN, and PBC influence the behavioral intentions simultaneously.

3.Methodology 3.1. Research framework Our study applies TPB as the primary research framework and will examine how the intentions of members are affected by decomposed attitudes (i.e., website service quality, website satisfaction, and website loyalty), SN, and PBC in a virtual community. Website Service Quality

H1

Website Satisfaction

H2

Website Loyalty

H3 Subjective Norms

H4

Intentions to become paying members

H5 Perceived Behavior Control

Figure 1. Research Framework Previous studies confirmed that the rewards of intrinsically motivated online content in a virtual community are invisible and uncountable because the online content is created by users automatically. In this study, the attitude was decomposed of website SQ, website satisfaction, and website loyalty. Therefore, based on the belief constructs of TPB, our study proposes that website SQ is used to measure the behavioral beliefs of online users. After users experience the services provided by websites, they will have specific positive or negative intentions about becoming paying members. Therefore, we adopt website SQ, website satisfaction, and loyalty to measure users’ attitudes [9]. In addition, we further explain the relationship between SN, PBC, and the intention to become paying members.

3.2. Research hypothesis Service quality positively influences satisfaction and consequently a firm’s profitability [10]. Moreover, the authors in reference [11] asserted a strong positive relationship between service quality and satisfaction. In the study, website SQ is defined as website efficiency, system stability, privacy, website design, and interaction fairness, which are referenced by [12][13]. Web quality-related research appears to have a significant positive influence on consumer satisfaction [14][15][16]. According to the above research, we suggest as follows:

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H1: Website SQ positively influences website satisfaction. Satisfaction is consumers’ valuation after their purchase and their response to their experience with the products or services [17]. Authors in reference [18] studied online website design in different cultures, including Canada, Germany, and China. The results showed that in different countries, the relationship between website satisfaction and e-loyalty is positively significant. Moreover, authors in another study [19] explored the relationship and moderators between online customers’ e-satisfaction and e-loyalty; the results showed the relationship between e-satisfaction and e-loyalty is significant and is moderated by inertia, convenience motivation, purchase size, and perceived value. Thus, we suppose the following: H2: Website satisfaction positively influences website loyalty. An individual’s behavior intention can be predicted by his or her attitude [7][8]. Authors in reference [20] also proposed four stages of membership development for virtual communities. They indicated that when members have gradually formed loyalty to the virtual community they will have higher intentions and enthusiastically buy related products and services from the virtual community. Consequently, we contend as follows: H3: Website loyalty positively influences intentions to become paying members. Individuals’ behavioral intentions can be predicted by SN [5][7][18]. TPB explains when some behaviors are expected or not expected by social groups and shows that individuals will easily follow social pressure to engage or not engage in their behaviors. Authors in reference [21] applied TRA to understand consumer behaviors in the online channel use context in a multi-channel environment. The study demonstrated that SN positively affects consumer’s behavioral intention to use online channel. Hence, we suggest as follows: H4: SN positively influences intentions of becoming paying members. Based on TPB, individuals’ behavioral intentions can be predicted by PBC. TPB explains that the cognitive function of individuals’ control abilities will directly affect their real intention and behaviors to do something [7][8]. After acclimatizing TPB as the research framework to explain the behaviors of consumers in online shopping, some scholars recognized the positive relationship between PBC and intentions in online shopping [22][23]. Therefore, we hypothesize as follows: H5: PBC positively influences intentions of becoming paying members.

3.3.Website introduction and data collection In this study, we adopted convenience sampling and cooperated with a Taiwan popular commercial virtual community. A link to the website was posted in our questionnaire on the main webpage of the website to invite the website users to complete our questionnaire. Target users were those individuals who used the case firm’s services. We collected a total of 576 questionnaires from August 2009 to September 2009, and we eliminated invalid questionnaires. A total of 532 valid questionnaires were ultimately collected. The collection rate was 92%.

4. Data analysis and empirical evidence 4.1. Descriptive statistics In Table 1, we see that most of respondents are females (64.4%), and 43.2% are between the ages of 20 and 30 years old. In addition, most respondents are undergraduates (38%). More than 53.3% of respondents have experienced the site for more than 1 month, and 56.2% respondents will go to the site at least once per day. Nearly 41% of respondents spent more than 1 hour on the site.

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Table 1. Descriptive Statistics of Respondents’ Characteristics Demographic variables Gender

Age

Education

Degree of the Site Experience

Usage frequency of the Site website

Duration on the Site per visit

Category Male Female 45 Elementary Junior High Senior High Junior College Under Graduate Graduate < One Week 1 week to 1 month 1 months to 3 months 3 months to 6 months 6 months to 1 year 1 year to 2 years > 2 years At least 1 days per time 2-3 days for per time 1 week per time 2-3 week per time Very rarely < 3 minutes 3-10 minutes 10-30 minutes 30-60 minutes > One hour

Frequency (n=532) 189 343 28 106 120 110 75 51 22 21 10 34 144 92 203 49 107 142 95 44 55 62 29 299 128 40 20 45 15 78 120 104 216

Percentage 35.60% 64.40% 5.30% 19.90% 22.50% 20.70% 14.10% 9.50% 4.12% 4% 1.90% 6.32% 27% 17.30% 38% 9.18% 20.10% 26.60% 17.80% 8.20% 10.30% 11.60% 5.40% 56.20% 24.10% 7.50% 3.80% 8.40% 2.80% 14.60% 22.50% 19.50% 40.60%

4.2. Data analysis methods We chose partial least squares (PLS) for structure equation analysis to test the causal effects of our model. PLS is a kind of multivariate data analysis based on the hypothesis of a linear relationship to construct the relationship of whole research structure. It includes a measurement model and a structure model [24]. The measurement model represents the confirmatory factor analysis (CFA) to test the relationship between observed and latent variables. The structure model, which is based on certain assumptions, explores the causal effects of latent variables. We applied SmartPLS 2.0 to analyze the measurement model and the structure model of the study. The validity of the constructs was assessed in terms of unidimensionality, internal consistency, convergent validity, and discriminant validity.

4.3. Measurement model We followed a two-stage analytical procedure [24][25]. Confirmatory factor analysis (CFA) was used in the first stage to assess the constructs’ relationship for convergent validity and discriminant

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validity to make sure our latent constructs can be measured correctly and effectively. The structural relationships were examined according to the hypotheses of our study in the second stage. To verify the construct validity (including convergent validity and discriminant validity) of our model, we collected 253 records from the beginning of collection to be the first group. This was the calibration sample to build the hypothesis theory model and test the internal consistency. The second group included 279 records collected between the time for collection of the first group and the final data collection. This group was the validation sample to test our research model by PLS. Because the website SQ contains sub-constructs of website efficiency, system availability, privacy, website design, and interaction fairness, we applied high-order factor analysis with the website SQ to ensure all sub-constructs had construct validity. All of the indicators are suitable for the ranges of the suggested value. Therefore, our website SQ shows acceptable goodness of fit. The coefficient loadings of high-order website SQ are shown in Table 2. Table 2. Factor Loadings of High-Order CFA for Website SQ Factor loading High-order First-order T-Value (Standardized Factor factor Coefficient) Website Efficiency System Stability Privacy Website Design Interaction Fairness

Website Service Quality

0.81

40.40***

0.76

32.24***

0.80 0.82

51.77*** 58.38***

0.83

63.10***

***：α=0.01；**：α=0.05；*：α=0.1

The statistic results indicate that CFA also provides a good fit to the high-order CFA. Table 3 lists the results of the CFA. Composite reliability (CR) is the indicator used to test the latent constructs’ reliability. The higher the values of CR, the more internal consistency of the latent constructs. Reference [24] proposed that CR must reach the value of 0.6. Average Variance Extracted (AVE) was utilized to calculate the explanation power of observed variables to latent constructs. The higher values of AVE, the more convergent validity exists for the latent constructs. The values of AVE should be more than 0.5 [26]. Table 3. Correlation Matrix and Composite Factor Reliability Scoresa Composite Reliability

Cronbach ’s alpha

Mean

STD

1

1.Website SQ

0.91

0.87

3.73

0.73

0.81

2.Website Satisfaction

0.90

0.86

4.08

0.77

0.68

0.83

3.Website Loyalty

0.94

0.92

4.09

0.75

0.61

0.70

0.87

4.SN

0.95

0.93

3.01

0.98

0.34

0.33

0.32

0.85

5.PBC

0.94

0.91

3.70

1.01

0.28

0.31

0.31

0.35

0.91

6.Intentions to become paying members

0.95

0.93

3.20

1.02

0.41

0.42

0.42

0.65

0.61

Latent Variable

a

2

3

4

5

6

0.92

Items in bold diagonal represent the square root of the average variance extracted scores (AVE). According to the CR and AVE values, all of our latent constructs reach the required value of 0.7 and 0.5, respectively. Acceptable value of an CR for perceptual measures should be more than 0.7; our study has reliability and convergent validity. In PLS, indicators of the internal consistency can be

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calculated using the composite reliability [27]. Acceptable values of composite reliability should exceed 0.7 [26]. For the discriminant analysis, the AVE may be compared with the square of the correlations among the latent variables[28]. An appropriate measurement model is the diagonal of Table 3, which contains the square root of the AVE. All AVE values are greater than the off-diagonal elements in the corresponding rows and columns, demonstrating the discriminant validity.

4.4.Structural model After testing CR, convergent validity, and discriminant validity, the proposed model tests the causality of our research framework by adopting PLS. The statistics indicate that it also provides a good fit to the structure model. The results of PLS analysis are shown in Fig. 2. All of the causal relationships of our constructs in the structure model are positively significant.

0.76 (27.85)***

0.68 (15.536)*** Website Service Quality

Website Satisfaction 2 R =0.432 0.41 (10.382)*** Subjective Norms

Website Loyalty 2 R =0.583 0.14 (30.56)***

Intentions to become paying members 2 R =0.576

0.45 (10.506)*** Perceived Behavior Control ***：α=0.01；**：α=0.05；*：α=0.1 Parentheses is t-value.

Figure 2. SEM analysis of Research Model

5. Conclusion Our research results support some scholars’ conclusions that users will associate website satisfaction with the website SQ [14][15][16] and affect website loyalty directly [17][18][19]. After potential users join a virtual community and become members, the users will perceive the SQ from the websites and have higher satisfaction with the virtual community. Consequently, users will have greater loyalty for the website. Our study also verifies the positive effects of website loyalty on intentions of becoming paying members. This is the same result described by authors in reference [20]. According to our results, paying members who are highly respected or regarded by non-paying users can influence non-paying members to develop greater intention to become paying members due to SN. SN will influence the users to have higher intentions of becoming paying members. The result (i.e., that SN has a positive relationship with online shopping behavior) is also confirmed by authors in reference [21]. Moreover, our study also appears to show that online users with higher PBC will demonstrate greater intentions of becoming paying members. Our research results are consistent with previous findings that consumers’ PBC has a positive relationship with their intentions [22][23].

6.Acknowledgements The authors gratefully acknowledge the support of the National Science Council (Grant 99-2410-H-004-150) and Sayling Wen Culture & Education Foundation (Grant 98A009-7).

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